In today's digital landscape, artificial intelligence (AI) has become an integral part of many mobile applications. By leveraging machine learning frameworks, developers can create intelligent systems that automate tasks, provide personalized experiences, and analyze vast amounts of data with unprecedented speed and accuracy.

However, building AI into mobile apps used to be a challenging task due to the limited processing power of smartphones. In the past, API requests were used to connect with more powerful servers that handled complex calculations, sending output data back to the mobile device for actioning. But times have changed, and we're now seeing a plethora of machine learning frameworks optimized for mobile performance.

In this article, we'll explore the top 10 machine learning frameworks for mobile apps, categorized into two sections: desktop-based frameworks designed for advanced AI capabilities and mobile-specific frameworks optimized for performance.

Desktop-Based Machine Learning Frameworks

These powerful toolkits are designed to handle large datasets and complex calculations, making them ideal for building advanced AI applications. Here are some of the most popular desktop-based machine learning frameworks:

  • Google's TensorFlow: With over 50% market share on GitHub, TensorFlow is a powerhouse in the AI community. Its extensive library of add-ons, plugins, and modules makes it possible to build a wide range of AI-powered applications, from product recommendations to facial recognition.
  • Amazon Machine Learning (AML): AML offers a robust set of features for training neural networks, including deployment on AWS cloud and integration with Alexa services. The community-driven framework has contributed numerous examples, add-ons, and tutorials to help developers build complex AI applications.
  • Microsoft Cognitive Toolkit: This powerful toolkit is particularly popular among enterprise-grade developers, allowing them to create various types of neural networks, such as feedforward and recurrent networks.
  • MXNet: As one of the most popular frameworks on GitHub, MXNet offers a unique blend of portability, flexibility, and community-driven content. It allows developers to write in different languages, including Python, Rust, Scala, Go, and JavaScript.

Mobile-Specific Machine Learning Frameworks

These frameworks are optimized for mobile devices, providing seamless integration with existing applications. Here are some of the top mobile-specific machine learning frameworks:

  • Google's TensorFlow Lite: Designed specifically for Android phones, TensorFlow Lite offers low-latency image processing, hardware acceleration on Android devices, and quantized kernels for faster calculations.
  • Caffe2: This modular approach to machine learning makes it possible to pick and choose which models and tools are needed for any given project. The main feature is mobile deployment, allowing developers to run neural network computations in real-time on mobile devices.
  • Bender: By leveraging Apple's Core ML, Bender allows iPhone users to run machine learning algorithms from their device's GPU, making it a promising framework for mobile AI applications.
  • Quantized-CNN: This lightweight framework offers an accurate solution for image classification, running fully on the device with minimal loss in accuracy compared to heavier frameworks.

Conclusion

While we're seeing significant advancements in machine learning frameworks for mobile apps, it's clear that most large corporations still rely on API calls to communicate with powerful servers. However, as the popularity of AI-powered applications continues to grow, we can expect to see more optimized frameworks and libraries emerge, enabling seamless integration with existing applications.

About the Author

Mark has been developing for the web since 2001, always with a penchant for open-source technologies such as PHP. Since 2010, he has been working full-time with app development, currently employed at nodesagency, a leading European app agency. He also contributes to various AI-related projects and communities.